Self-Guidance: Boosting Flow and Diffusion Generation on Their Own (2412.05827v4)

Abstract: Proper guidance strategies are essential to achieve high-quality generation results without retraining diffusion and flow-based text-to-image models. Existing guidance either requires specific training or strong inductive biases of diffusion model networks, potentially limiting their applications. Motivated by the observation that artifact outliers can be detected by a significant decline in the density from a noisier to a cleaner noise level, we propose Self-Guidance (SG), which improves the image quality by suppressing the generation of low-quality samples. SG only relies on the sampling probabilities of its own diffusion model at different noise levels with no need of any guidance-specific training. This makes it flexible to be used in a plug-and-play manner with other sampling algorithms. We also introduce a more efficient approximation of SG, named SG-prev, which reuses the output from the immediately previous diffusion step to avoid doubling sampling time. We conduct experiments on text-to-image and text-to-video generation with different architectures, including UNet and transformer models. With open-sourced diffusion models such as Stable Diffusion 3.5 and FLUX, SG exceeds existing algorithms on multiple metrics, including both FID and Human Preference Score. SG-prev also achieves strong results over both the baseline and the SG with only one forward pass. Moreover, we find that SG and SG-prev both have a surprisingly positive effect on the generation of physiologically correct human body structures such as hands, faces, and arms, showing their ability of eliminating human body artifacts with minimal efforts. We will release our code along with this paper.